Notebook

load("data.RData")
suppressMessages(require(knitr, quietly = T))
suppressMessages(require(kableExtra, quietly = T))
suppressMessages(require(rmarkdown, quietly = T))
suppressMessages(require(plyr, quietly = T))
suppressMessages(require(dplyr, quietly = T))
suppressMessages(require(ggplot2, quietly = T))
suppressMessages(require(GGally, quietly = T))
suppressMessages(require(parallel, quietly = TRUE))

Raw Data

We show the raw data below. There were 196 rows and 42 columns, consisting of 28 patients taken over 7 timepoints. However, there is a significant amount of missing data, resulting in only 120 usable datapoints.

missing.ix <- Reduce(intersect, apply(df[,14:42], 2, function(x) which(is.na(x))))
df.raw <- df[-missing.ix,]
df.raw <- df.raw[order(df.raw$PatientID),]
rownames(df.raw) <- 1:nrow(df.raw)
kable(df.raw, 
      digits = 3,
      row.names = T,
      caption = "Raw Data"
      ) %>%
    kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                  font_size = 12) %>%
    scroll_box(width = "100%", height = "300px") 

Raw Data

	PatientID	Age	AgeGreater60	Sex	LowIntermacs	InterMACS	RVAD	Sensitized	VAD Indication	Device Type	Survival	Outcome	Time	num Total PBMC	num lymph	lymph	live lymph	CD3 of live lymph	CD19 of live lymph	CD19+CD27-	CD19+CD27+	CD27+38++plasma blasts	CD27-38++ transitional	CD27-IgD+ mature naive	CD27+IgD- switched memory	CD27-IgD- switched memory	CD27+IgD+ unswitched memory	CD27+IgD-IgM+ switched memory	CD27+IgD+IgM+ nonswitched memory	CD19+27+IgG+IgM- memory	CD19+24dim38dim naive mature	CD19+24+38++transitional	CD19CD24hiCD38-memory	CD19+27-38+CD5+transitionals	CD19+CD268+	CD268 of +27-38++transitional	CD19+CD11b+	CD19+CD5+	CD19+CD27+CD24hi	CD19+CD5+CD24hi	CD19+CD5+CD11b+	CD19+27+IgD-38++IgG ASC
1	1	65	older	Male	High	2	No	NA	BTT	HMII	alive	Alive s/p OHT	0	169154	35496	20.98	99.53	25.37	19.82	86.02	13.98	1.26	2.54	57.81	11.74	28.06	2.39	21.74	14.36	0.72	81.05	2.86	13.60	0.58	95.22	84.83	10.47	4.33	8.50	1.39	3.13	1.09
2	1	65	older	Male	High	2	No	NA	BTT	HMII	alive	Alive s/p OHT	1	63082	9915	15.72	99.26	32.45	26.26	90.06	9.94	0.89	2.09	54.27	7.70	35.71	2.32	31.10	20.47	1.57	84.06	2.59	10.79	0.26	97.02	92.59	7.50	3.13	6.42	1.32	2.44	1.49
3	1	65	older	Male	High	2	No	NA	BTT	HMII	alive	Alive s/p OHT	3	75921	21721	28.61	99.31	24.86	33.01	86.38	13.62	1.54	1.26	45.74	10.98	40.44	2.84	31.80	18.04	1.15	86.21	4.82	6.56	0.24	90.41	73.33	10.00	7.25	10.12	3.22	5.67	2.05
4	1	65	older	Male	High	2	No	NA	BTT	HMII	alive	Alive s/p OHT	8	213808	36002	16.84	99.19	35.95	14.55	67.94	32.06	4.06	2.17	27.92	28.92	39.64	3.52	20.31	9.10	1.33	68.69	7.10	20.42	1.88	87.38	44.25	10.78	11.12	19.15	5.54	7.35	1.07
5	1	65	older	Male	High	2	No	NA	BTT	HMII	alive	Alive s/p OHT	21	106970	31867	29.79	99.18	42.36	15.02	84.69	15.31	1.94	2.11	55.41	13.14	29.13	2.32	20.22	12.88	2.08	83.21	5.20	9.50	1.10	94.44	73.00	9.04	6.99	9.22	2.80	4.78	1.44
6	2	81	older	Male	Low	3	No	NA	DT	HMII	dead	Died	0	119377	77509	64.93	99.61	58.50	7.99	62.67	37.33	9.10	4.82	36.73	31.90	25.62	5.74	11.47	8.95	0.40	69.54	7.69	9.41	2.61	76.44	57.91	23.29	20.76	15.39	5.42	14.90	2.60
7	2	81	older	Male	Low	3	No	NA	DT	HMII	dead	Died	3	96940	57658	59.48	99.70	55.52	9.21	72.15	27.85	3.85	3.82	48.41	21.75	23.49	6.35	12.10	16.04	0.28	75.30	3.72	6.65	2.11	80.52	67.33	15.80	15.23	6.71	1.32	8.86	0.26
8	2	81	older	Male	Low	3	No	NA	DT	HMII	dead	Died	5	154373	73969	47.92	99.65	45.18	8.77	67.36	32.64	6.59	2.77	39.10	26.28	27.95	6.67	17.89	15.71	0.39	71.57	6.26	9.88	1.78	81.86	72.07	20.12	20.17	14.80	5.99	14.65	3.71
9	2	81	older	Male	Low	3	No	NA	DT	HMII	dead	Died	8	155610	63375	40.73	99.51	38.42	12.40	71.69	28.31	4.36	2.62	37.82	23.43	33.50	5.24	17.37	14.92	0.60	65.04	2.80	11.23	2.54	83.55	37.56	15.55	15.45	9.55	2.66	10.91	0.28
10	2	81	older	Male	Low	3	No	NA	DT	HMII	dead	Died	14	244245	115109	47.13	99.47	47.40	9.37	67.50	32.50	3.99	2.08	50.08	26.01	17.22	6.68	15.34	15.25	0.23	77.86	3.21	10.49	1.48	82.13	59.19	21.45	19.77	14.45	2.57	14.94	0.94
11	3	58	younger	Male	Low	3	Yes	No	BTT	HMII	alive	Alive s/p OHT	1	246811	86529	35.06	93.49	67.96	17.48	81.66	18.34	2.05	6.85	51.33	13.69	29.91	5.08	15.68	5.94	0.19	61.95	0.76	13.51	4.07	92.62	34.06	6.90	12.07	8.00	2.54	3.85	0.26
12	3	58	younger	Male	Low	3	Yes	No	BTT	HMII	alive	Alive s/p OHT	3	406771	90343	22.21	97.34	49.28	17.02	75.04	24.96	4.47	18.36	28.45	21.86	46.07	3.62	24.97	4.33	0.13	47.41	1.64	19.70	18.15	72.76	8.08	25.69	28.35	13.95	4.66	19.20	0.41
13	3	58	younger	Male	Low	3	Yes	No	BTT	HMII	alive	Alive s/p OHT	21	330191	81636	24.72	94.57	45.14	14.70	70.51	29.49	8.94	16.23	38.92	23.53	30.83	6.72	23.93	7.07	0.18	46.63	2.35	16.12	8.59	74.45	39.90	24.19	21.18	12.62	3.26	12.93	0.38
14	4	67	older	Male	Low	3	No	Yes	BTT	HVAD	alive	Alive s/p OHT	0	309890	41896	13.52	97.03	47.13	8.08	77.31	22.69	2.74	3.29	59.03	19.80	19.37	1.80	21.10	4.46	19.79	71.98	2.41	17.39	0.20	95.80	75.00	5.79	1.89	16.45	0.91	0.67	0.77
15	4	67	older	Male	Low	3	No	Yes	BTT	HVAD	alive	Alive s/p OHT	1	274228	15653	5.71	97.85	35.76	28.87	51.45	48.55	2.60	2.13	30.85	43.13	22.77	3.26	20.09	3.92	30.51	57.58	1.31	28.20	0.18	91.23	59.57	12.08	2.04	34.44	0.38	0.16	0.16
16	4	67	older	Male	Low	3	No	Yes	BTT	HVAD	alive	Alive s/p OHT	3	591939	57589	9.73	96.76	38.20	42.54	75.71	24.29	2.14	1.33	53.03	21.26	24.16	1.55	25.50	4.02	20.27	67.18	1.14	21.71	0.11	95.28	66.98	3.79	2.43	17.70	0.65	0.84	0.18
17	4	67	older	Male	Low	3	No	Yes	BTT	HVAD	alive	Alive s/p OHT	5	189634	46711	24.63	97.74	34.84	37.32	75.30	24.70	0.70	0.55	46.97	22.05	30.28	0.70	23.67	1.56	31.44	73.33	0.75	18.59	0.00	84.61	86.17	3.44	2.11	18.68	0.94	0.63	0.69
18	4	67	older	Male	Low	3	No	Yes	BTT	HVAD	alive	Alive s/p OHT	14	182319	40475	22.20	96.28	43.36	11.77	69.34	30.66	1.59	0.89	52.64	27.32	18.56	1.48	22.79	2.88	28.61	75.47	0.33	14.54	0.06	95.90	53.66	7.81	3.27	21.04	1.57	1.50	0.72
19	5	65	older	Female	High	2	No	Yes	BTT	HMII	alive	Alive s/p OHT	0	1073919	336538	31.34	100.00	63.29	14.80	93.05	6.95	0.73	8.05	75.49	4.55	19.16	0.80	7.25	8.78	28.04	88.72	2.51	5.18	1.02	90.49	68.18	2.32	4.09	1.69	3.48	1.27	5.78
20	5	65	older	Female	High	2	No	Yes	BTT	HMII	alive	Alive s/p OHT	1	270301	97229	35.97	91.63	18.43	61.67	97.68	2.32	0.20	5.85	69.45	1.63	28.19	0.73	27.57	21.43	17.05	84.32	7.26	2.73	0.84	99.44	97.60	0.54	2.64	1.51	3.63	0.29	3.40
21	5	65	older	Female	High	2	No	Yes	BTT	HMII	alive	Alive s/p OHT	3	499016	78019	15.63	89.41	25.83	51.38	92.98	7.02	0.32	4.66	51.34	6.31	41.59	0.76	36.49	5.96	22.81	73.48	6.73	7.93	0.33	98.78	96.52	1.31	2.06	5.19	2.68	0.59	2.45
22	5	65	older	Female	High	2	No	Yes	BTT	HMII	alive	Alive s/p OHT	8	115825	32513	28.07	91.04	44.61	15.76	80.52	19.48	4.16	7.82	49.91	18.37	30.52	1.20	14.87	3.33	30.19	61.08	8.70	12.56	1.58	89.80	76.71	2.87	6.07	11.12	8.08	1.59	1.17
23	5	65	older	Female	High	2	No	Yes	BTT	HMII	alive	Alive s/p OHT	21	407080	97651	23.99	91.29	66.96	12.20	84.66	15.34	2.88	5.60	61.89	13.73	22.65	1.74	20.16	7.23	29.39	68.49	5.28	10.60	1.17	92.50	54.35	3.48	6.53	9.61	8.34	1.85	2.83
24	6	43	younger	Male	High	2	Yes	No	BTT	HMII	alive	Alive s/p OHT	0	515760	266115	51.60	96.30	69.66	8.57	72.22	27.78	6.26	4.77	43.09	26.01	28.26	2.63	16.67	2.67	28.25	50.35	0.41	12.78	4.47	84.76	19.47	9.56	4.55	8.29	0.87	2.18	0.75
25	6	43	younger	Male	High	2	Yes	No	BTT	HMII	alive	Alive s/p OHT	8	539521	223872	41.49	96.72	60.02	12.63	80.13	19.87	1.66	2.48	43.56	19.52	35.24	1.68	20.46	3.67	20.54	55.76	0.18	10.80	2.43	87.82	13.25	6.55	5.96	4.65	0.95	2.51	0.65
26	6	43	younger	Male	High	2	Yes	No	BTT	HMII	alive	Alive s/p OHT	21	564317	216312	38.33	97.40	55.80	4.83	74.19	25.81	5.35	5.77	37.90	25.72	34.76	1.62	17.99	2.18	31.75	61.61	0.77	13.44	6.97	76.55	6.97	10.34	11.14	4.46	0.94	5.14	2.43
27	7	49	younger	Male	Low	3	No	No	BTT	HVAD	alive	Alive s/p OHT	0	89251	65181	73.03	76.76	84.53	1.15	74.65	25.35	1.91	1.39	55.03	22.40	19.62	2.95	15.65	6.80	25.85	70.49	0.87	23.26	0.23	83.16	25.00	8.85	5.03	11.63	1.56	2.95	3.08
28	7	49	younger	Male	Low	3	No	No	BTT	HVAD	alive	Alive s/p OHT	3	375167	234817	62.59	98.46	60.29	10.18	87.72	12.28	0.46	0.48	68.54	9.90	19.03	2.54	30.58	19.69	15.54	72.77	0.13	20.35	0.03	92.56	36.28	3.75	2.70	9.17	1.19	1.27	0.63
29	7	49	younger	Male	Low	3	No	No	BTT	HVAD	alive	Alive s/p OHT	5	256306	162176	63.27	93.82	68.43	8.45	85.45	14.55	0.70	0.53	71.99	11.98	13.37	2.66	32.41	17.96	12.46	69.74	0.41	24.63	0.02	95.72	44.12	4.47	2.23	12.99	1.30	1.20	0.77
30	7	49	younger	Male	Low	3	No	No	BTT	HVAD	alive	Alive s/p OHT	8	463136	330714	71.41	98.79	70.51	7.30	87.51	12.49	1.78	0.58	69.60	10.61	17.82	1.97	29.83	14.55	12.59	75.03	0.15	21.61	0.07	93.59	30.94	4.05	3.07	10.00	1.08	1.35	0.62
31	8	43	younger	Male	Low	3	No	No	BTT	HMII	alive	Alive s/p OHT	0	92221	27988	30.35	85.45	55.78	1.51	85.56	14.44	0.28	0.28	55.28	4.72	39.72	0.28	0.00	1.89	1.89	89.44	0.00	2.78	0.00	15.00	0.00	3.33	0.83	0.28	0.28	3.33	0.00
32	8	43	younger	Male	Low	3	No	No	BTT	HMII	alive	Alive s/p OHT	1	273746	46755	17.08	93.25	50.14	7.57	88.27	11.73	0.09	2.12	54.52	3.36	42.00	0.12	8.40	6.87	0.00	92.00	0.12	3.45	0.00	3.94	11.43	1.12	0.64	2.76	0.33	2.15	0.00
33	8	43	younger	Male	Low	3	No	No	BTT	HMII	alive	Alive s/p OHT	3	156053	71752	45.98	78.38	35.43	27.12	78.57	21.43	0.24	0.60	58.56	5.80	18.33	17.31	12.67	72.96	0.53	94.06	0.49	5.11	0.28	22.79	27.47	0.81	5.53	0.01	0.29	0.19	0.00
34	8	43	younger	Male	Low	3	No	No	BTT	HMII	alive	Alive s/p OHT	8	156053	71752	45.98	78.38	35.43	27.12	78.57	21.43	0.24	0.60	58.56	5.80	18.33	17.31	12.67	72.96	0.53	94.06	0.49	5.11	0.28	22.79	27.47	0.81	5.53	0.01	0.29	0.19	0.00
35	9	60	younger	Female	High	2	Yes	Yes	BTT	PVAD	dead	Died	3	116462	41076	35.27	95.44	33.79	25.24	84.51	15.49	2.92	0.99	45.34	13.19	39.12	2.36	17.56	8.75	19.73	79.35	5.17	10.80	0.71	90.04	29.59	10.16	6.81	8.21	3.37	4.86	4.47
36	9	60	younger	Female	High	2	Yes	Yes	BTT	PVAD	dead	Died	5	222650	85649	38.47	98.40	32.09	19.82	77.54	22.46	5.02	1.10	43.96	19.20	33.50	3.34	17.47	9.77	15.63	71.89	6.68	14.08	0.70	86.49	27.87	13.04	9.84	12.30	4.35	7.98	6.39
37	9	60	younger	Female	High	2	Yes	Yes	BTT	PVAD	dead	Died	8	102274	63057	61.65	97.73	27.89	20.66	73.75	26.25	7.33	3.45	35.79	21.92	37.73	4.56	9.97	8.20	16.96	63.96	2.40	11.16	3.07	73.97	5.92	16.69	17.77	11.74	4.90	12.19	6.09
38	9	60	younger	Female	High	2	Yes	Yes	BTT	PVAD	dead	Died	14	133530	81665	61.16	96.46	22.03	16.15	35.30	64.70	13.74	2.33	8.48	52.28	26.37	12.87	5.29	5.79	19.15	47.48	5.65	17.83	5.43	39.88	6.08	52.08	62.11	37.45	21.95	50.15	3.84
39	10	36	younger	Male	High	2	Yes	NA	BTT	PVAD	alive	Alive s/p OHT	0	481928	162052	33.63	99.70	64.73	5.05	3.91	96.09	8.89	1.34	0.72	94.99	3.14	1.15	4.07	0.84	0.32	90.28	2.84	1.58	1.27	6.82	9.17	14.61	90.11	4.95	5.71	13.01	0.51
40	10	36	younger	Male	High	2	Yes	NA	BTT	PVAD	alive	Alive s/p OHT	8	38113	21486	56.37	97.86	58.44	7.88	20.47	79.53	8.09	1.69	1.21	75.00	18.96	4.83	10.07	2.59	0.53	82.19	2.29	9.00	3.59	15.16	0.00	23.07	63.95	4.77	6.52	16.91	0.40
41	10	36	younger	Male	High	2	Yes	NA	BTT	PVAD	alive	Alive s/p OHT	21	80991	19210	23.72	99.00	50.61	3.50	12.33	87.67	14.74	4.81	1.05	85.41	11.13	2.41	8.48	0.52	1.56	80.00	8.27	3.76	7.41	13.68	0.00	30.83	77.74	10.83	14.14	19.85	2.81
42	11	61	older	Male	High	2	No	No	BTT	HMII	alive	Alive s/p OHT	0	411616	119210	28.96	90.82	73.63	5.88	74.30	25.70	2.80	6.46	57.94	19.20	18.72	4.13	19.10	15.42	15.30	53.51	13.78	25.19	0.21	47.23	53.77	5.75	5.92	22.63	3.46	2.55	0.74
43	11	61	older	Male	High	2	No	No	BTT	HMII	alive	Alive s/p OHT	1	430509	79492	18.46	89.31	52.10	20.45	87.13	12.87	1.23	4.76	58.11	9.15	30.55	2.20	29.86	18.05	10.37	63.63	10.23	17.43	0.06	74.05	72.50	2.36	2.98	10.68	1.72	1.03	2.89
44	11	61	older	Male	High	2	No	No	BTT	HMII	alive	Alive s/p OHT	3	395713	119670	30.24	97.90	23.79	6.27	82.17	17.83	0.79	1.99	49.95	13.01	33.91	3.13	28.36	16.83	5.00	56.33	7.13	26.73	0.02	79.50	82.88	1.96	5.55	16.30	3.61	1.88	0.42
45	11	61	older	Male	High	2	No	No	BTT	HMII	alive	Alive s/p OHT	8	329840	73468	22.27	98.63	70.61	3.73	51.50	48.50	8.32	3.77	33.46	41.29	20.92	4.33	25.19	5.89	24.07	46.40	3.73	34.64	0.07	64.14	28.43	10.35	9.61	38.78	4.62	5.55	2.91
46	11	61	older	Male	High	2	No	No	BTT	HMII	alive	Alive s/p OHT	14	350294	88823	25.36	97.63	74.37	4.37	73.35	26.65	4.91	4.83	56.54	21.32	19.84	2.30	34.68	11.25	18.37	61.98	5.78	20.55	0.04	63.91	47.54	6.81	6.17	21.77	2.90	2.74	3.00
47	12	38	younger	Male	High	2	Yes	Yes	BTT	TAH	dead	Died s/p OHT	0	128451	60927	47.43	96.82	60.77	4.70	77.26	22.74	2.17	1.95	55.58	18.40	21.54	4.47	27.86	18.89	15.33	69.90	0.94	24.40	0.14	70.73	1.85	8.01	8.66	20.21	5.45	3.32	0.39
48	12	38	younger	Male	High	2	Yes	Yes	BTT	TAH	dead	Died s/p OHT	1	142796	22573	15.81	80.67	47.31	2.28	63.37	36.63	9.16	6.51	26.02	33.49	37.11	3.37	38.13	7.50	13.13	53.25	5.78	26.27	1.57	40.24	0.00	27.71	22.17	29.16	18.80	15.66	8.11
49	12	38	younger	Male	High	2	Yes	Yes	BTT	TAH	dead	Died s/p OHT	3	157167	38008	24.18	93.47	34.86	3.95	69.71	30.29	3.92	3.92	33.64	26.30	35.78	4.28	39.54	11.49	14.94	66.86	2.85	24.31	1.04	64.50	7.27	21.60	20.53	27.66	14.54	14.18	2.12
50	12	38	younger	Male	High	2	Yes	Yes	BTT	TAH	dead	Died s/p OHT	5	121823	23097	18.96	93.48	2.51	2.87	99.52	0.48	0.00	0.00	0.97	0.48	98.55	0.00	50.00	25.00	25.00	94.35	0.00	2.58	0.00	0.16	0.00	17.45	87.40	0.00	0.16	18.26	0.00
51	12	38	younger	Male	High	2	Yes	Yes	BTT	TAH	dead	Died s/p OHT	8	196515	88579	45.07	85.54	17.17	3.28	59.38	40.62	3.21	1.41	35.44	35.28	23.87	5.42	62.85	12.32	3.71	66.05	4.38	23.22	0.27	74.53	31.43	25.51	28.12	39.78	25.79	21.57	0.79
52	13	61	older	Male	High	1	Yes	No	BTT	TAH	alive	Alive s/p OHT	1	87827	15751	17.93	76.38	31.60	12.17	77.46	22.54	6.35	2.53	50.34	20.22	27.05	2.39	19.68	7.62	19.05	75.55	7.38	9.49	0.71	94.26	89.19	11.68	17.96	18.78	6.97	10.86	12.46
53	13	61	older	Male	High	1	Yes	No	BTT	TAH	alive	Alive s/p OHT	3	55539	13608	24.50	79.00	28.02	15.78	81.90	18.10	6.13	2.42	66.92	14.33	14.98	3.77	20.81	17.45	18.12	78.01	7.08	8.61	0.72	94.04	70.73	11.32	16.69	15.21	5.78	10.50	16.39
54	13	61	older	Male	High	1	Yes	No	BTT	TAH	alive	Alive s/p OHT	8	295005	36785	12.47	94.58	36.55	9.14	86.32	13.68	2.45	1.82	37.01	12.52	49.18	1.29	16.24	4.71	12.47	8.36	1.73	6.64	0.81	84.78	46.55	12.52	9.28	5.94	1.73	4.47	2.49
55	13	61	older	Male	High	1	Yes	No	BTT	TAH	alive	Alive s/p OHT	14	60695	20471	33.73	94.91	6.93	7.42	83.97	16.03	3.40	0.69	24.36	14.71	59.33	1.60	18.43	5.07	11.52	54.82	2.91	16.10	0.50	82.44	30.00	51.08	13.25	10.41	3.68	11.31	6.06
56	14	39	younger	Female	Low	3	Yes	No	BTT	HMII	dead	Died post OHT	0	52016	30920	59.44	95.77	55.57	24.62	58.73	41.27	2.14	0.97	53.01	5.05	5.45	36.50	9.39	81.29	2.88	81.48	5.65	9.02	0.48	97.75	71.83	0.67	22.89	0.34	1.78	0.49	NA
57	14	39	younger	Female	Low	3	Yes	No	BTT	HMII	dead	Died post OHT	1	75516	48801	64.62	92.13	52.90	18.64	70.39	29.61	2.92	2.21	62.18	5.13	7.98	24.70	13.79	69.54	3.19	84.14	3.64	6.36	0.41	94.93	43.78	0.57	15.61	0.23	0.91	0.36	NA
58	14	39	younger	Female	Low	3	Yes	No	BTT	HMII	dead	Died post OHT	3	250612	130454	52.05	95.75	60.05	3.29	46.04	53.96	1.34	3.89	33.08	32.47	8.29	26.15	3.56	34.61	0.09	76.71	0.39	13.20	0.28	39.79	14.38	2.26	1.56	10.45	2.89	3.04	0.08
59	14	39	younger	Female	Low	3	Yes	No	BTT	HMII	dead	Died post OHT	8	212478	120477	56.70	85.12	48.36	12.51	79.57	20.43	1.35	2.14	58.73	12.87	18.44	9.96	5.63	34.32	0.11	86.74	0.41	6.88	0.15	35.30	53.09	1.23	0.75	2.36	1.04	1.47	NA
60	15	70	older	Female	High	2	No	NA	DT	HMII	alive	Alive	0	86135	70306	81.62	93.71	74.50	1.59	61.74	38.26	1.91	2.67	33.97	14.50	26.81	24.71	20.67	62.47	9.03	73.00	2.00	24.71	1.27	20.23	71.43	0.48	12.79	0.38	3.24	0.48	4.60
61	15	70	older	Female	High	2	No	NA	DT	HMII	alive	Alive	1	81004	41334	51.03	94.06	53.64	2.99	61.70	38.30	3.10	2.75	39.07	13.34	22.12	25.47	12.58	63.11	15.99	78.92	2.93	17.38	3.35	23.24	71.88	0.52	11.70	0.09	1.29	0.52	9.83
62	15	70	older	Female	High	2	No	NA	DT	HMII	alive	Alive	5	90024	39434	43.80	95.32	68.83	5.62	71.59	28.41	0.85	1.66	35.23	13.02	35.80	15.96	22.94	52.37	5.85	76.42	1.14	21.83	0.62	14.54	48.57	0.38	4.69	0.00	0.80	0.38	0.62
63	15	70	older	Female	High	2	No	NA	DT	HMII	alive	Alive	14	106791	80532	75.41	88.82	79.92	1.57	62.32	37.68	1.70	4.02	28.04	22.86	34.11	15.00	23.92	39.41	15.03	61.61	3.66	34.55	1.97	20.36	73.33	0.80	8.57	0.27	1.79	0.98	0.71
64	16	63	older	Male	High	1	No	NA	BTT	HMII	dead	Died	3	366713	77282	21.07	98.61	66.55	12.44	93.65	6.35	0.53	0.60	62.22	4.68	32.38	0.72	19.94	9.89	28.23	83.18	0.51	14.38	0.30	94.72	54.39	2.07	11.33	2.86	3.40	1.48	3.58
65	16	63	older	Male	High	1	No	NA	BTT	HMII	dead	Died	5	262251	55344	21.10	99.55	62.37	14.39	93.00	7.00	1.03	0.69	65.71	4.74	28.37	1.17	25.93	17.28	25.93	79.87	1.20	17.05	0.48	97.76	58.18	2.27	11.13	4.12	4.91	1.87	6.10
66	16	63	older	Male	High	1	No	NA	BTT	HMII	dead	Died	8	309193	61091	19.76	98.15	56.62	7.24	83.61	16.39	3.27	1.52	58.03	11.90	28.13	1.93	24.65	11.36	1.52	79.53	1.45	13.33	0.97	92.63	25.76	3.50	9.23	7.57	3.73	3.48	0.00
67	17	68	older	Male	Low	3	No	NA	DT	HMII	dead	Died	0	147997	54649	36.93	91.75	79.63	7.62	74.86	25.14	0.60	3.45	62.45	22.87	12.30	2.38	45.98	9.28	13.61	74.33	2.67	21.19	0.84	91.31	90.15	3.58	2.25	21.79	1.36	0.58	0.34
68	17	68	older	Male	Low	3	No	NA	DT	HMII	dead	Died	1	177687	56400	31.74	97.13	69.76	12.60	81.83	18.17	0.65	1.61	55.77	16.13	26.02	2.09	53.19	10.70	11.57	82.92	1.38	13.98	0.28	89.83	85.59	3.78	1.85	14.65	1.30	0.68	0.18
69	17	68	older	Male	Low	3	No	NA	DT	HMII	dead	Died	3	233155	67140	28.80	98.44	74.89	5.32	81.96	18.04	1.00	1.88	49.03	16.59	32.90	1.48	49.92	7.20	12.83	83.44	1.39	12.98	0.14	89.70	69.70	4.58	1.62	13.89	1.08	1.08	0.17
70	17	68	older	Male	Low	3	No	NA	DT	HMII	dead	Died	5	99257	29455	29.68	97.33	62.48	4.73	80.60	19.40	1.84	1.25	45.06	18.36	35.25	1.33	44.61	6.32	12.27	82.89	0.88	13.20	0.09	83.55	35.29	7.23	3.54	14.75	2.29	3.02	1.20
71	17	68	older	Male	Low	3	No	NA	DT	HMII	dead	Died	8	76358	18702	24.49	97.32	29.48	11.10	86.29	13.71	2.43	0.50	37.92	12.13	48.32	1.63	46.45	11.70	11.35	83.96	0.20	10.50	0.06	57.82	50.00	9.95	29.60	10.20	0.64	9.36	0.80
72	18	37	younger	Female	High	2	Yes	NA	BTT	PVAD	dead	Died s/p OHT	0	56374	12141	21.54	67.38	27.12	3.32	51.84	48.16	1.84	1.84	2.94	45.59	47.79	3.68	0.00	0.00	2.29	92.65	0.37	5.51	0.72	30.15	60.00	6.99	9.56	0.37	0.00	3.68	0.00
73	18	37	younger	Female	High	2	Yes	NA	BTT	PVAD	dead	Died s/p OHT	3	7018	3464	49.36	89.03	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
74	18	37	younger	Female	High	2	Yes	NA	BTT	PVAD	dead	Died s/p OHT	8	80210	21828	27.21	85.74	20.38	3.50	61.98	38.02	8.55	2.90	5.50	34.96	56.18	3.36	0.00	0.00	3.21	89.31	0.61	2.60	1.49	23.66	0.00	21.37	9.16	1.07	0.15	5.34	1.31
75	18	37	younger	Female	High	2	Yes	NA	BTT	PVAD	dead	Died s/p OHT	14	55068	19145	34.77	75.16	16.90	3.30	64.00	36.00	4.21	2.11	10.53	34.11	52.42	2.95	0.00	0.00	2.94	92.42	0.00	3.37	1.00	22.74	0.00	12.42	11.16	0.63	0.00	4.63	0.00
76	18	37	younger	Female	High	2	Yes	NA	BTT	PVAD	dead	Died s/p OHT	21	76673	23615	30.80	83.39	26.27	2.06	63.55	36.45	5.42	5.42	5.17	34.48	58.13	2.22	0.00	0.00	2.72	89.41	0.00	3.45	1.95	18.23	4.55	18.23	8.87	1.23	0.25	4.68	0.00
77	19	46	younger	Male	High	2	No	NA	DT	HMII	alive	Alive	0	226762	25868	11.41	89.41	25.31	12.05	55.06	44.94	11.31	2.69	14.79	42.57	39.95	2.69	19.87	3.88	32.88	68.95	10.48	13.57	0.59	80.62	22.67	31.91	19.38	15.58	10.37	16.55	8.18
78	19	46	younger	Male	High	2	No	NA	DT	HMII	alive	Alive	3	244049	39386	16.14	94.94	40.46	13.23	40.93	59.07	13.36	3.17	8.33	54.90	32.28	4.49	18.52	4.21	23.83	55.18	13.87	19.39	1.44	70.53	35.67	33.62	33.47	22.86	15.26	25.81	5.11
79	19	46	younger	Male	High	2	No	NA	DT	HMII	alive	Alive	5	344736	250121	72.55	89.83	7.54	3.81	45.84	54.16	3.96	1.53	13.39	52.70	32.18	1.72	67.14	2.92	2.44	74.68	6.81	13.91	0.21	63.33	58.02	33.05	30.94	19.83	13.96	26.74	0.22
80	19	46	younger	Male	High	2	No	NA	DT	HMII	alive	Alive	8	216934	140093	64.58	96.37	4.78	7.22	22.49	77.51	13.49	0.64	7.22	67.61	15.17	10.00	35.88	7.82	17.22	51.28	39.40	6.56	1.33	62.59	58.06	66.67	64.59	53.00	50.26	63.78	4.97
81	19	46	younger	Male	High	2	No	NA	DT	HMII	alive	Alive	14	215820	66785	30.94	92.54	17.20	6.01	25.88	74.12	9.15	1.45	3.09	71.72	22.44	2.74	65.43	3.42	0.04	69.38	15.93	8.85	0.74	50.58	24.07	52.78	48.88	32.28	28.19	44.04	0.04
82	19	46	younger	Male	High	2	No	NA	DT	HMII	alive	Alive	21	670692	523014	77.98	96.05	5.07	2.64	23.61	76.39	4.11	0.49	4.79	73.53	18.68	3.00	42.40	2.72	4.49	71.95	20.81	5.51	0.45	48.22	44.62	60.13	60.26	39.07	36.82	56.38	1.49
83	20	75	older	Male	High	2	No	NA	DT	HMII	alive	Alive	3	121794	28129	23.10	97.05	52.79	16.42	70.08	29.92	0.29	0.54	50.71	21.46	22.96	4.86	34.18	12.51	23.49	39.78	0.31	55.18	0.35	96.01	16.67	1.74	5.27	23.38	2.95	1.00	0.10
84	20	75	older	Male	High	2	No	NA	DT	HMII	alive	Alive	5	82157	17295	21.05	99.20	72.54	7.41	68.21	31.79	1.10	0.55	48.47	22.90	23.60	5.04	31.90	13.57	18.10	37.84	0.47	56.73	0.35	92.37	14.29	2.05	7.79	26.20	4.72	1.42	1.02
85	20	75	older	Male	High	2	No	NA	DT	HMII	alive	Alive	8	154810	27067	17.48	98.04	72.18	5.03	74.91	25.09	0.90	1.65	58.05	14.76	20.82	6.37	23.43	25.71	22.57	44.94	0.60	49.36	0.71	86.59	22.73	3.30	7.64	20.15	4.87	1.72	0.00
86	20	75	older	Male	High	2	No	NA	DT	HMII	alive	Alive	21	197936	15446	7.80	95.86	50.35	5.44	79.13	20.87	3.98	3.11	52.30	15.90	29.81	1.99	20.23	5.78	24.86	40.62	1.12	50.06	1.89	62.36	8.00	7.20	9.94	13.17	3.60	5.09	1.55
87	21	30	younger	Female	Low	3	No	Yes	BTT	HMII	alive	Alive s/p OHT	0	114586	35996	31.41	100.00	59.03	11.31	75.87	24.13	1.52	7.69	69.83	11.67	10.20	8.30	27.80	29.94	2.04	84.64	0.64	10.54	0.61	91.57	61.34	4.99	7.15	11.57	5.92	2.53	1.92
88	21	30	younger	Female	Low	3	No	Yes	BTT	HMII	alive	Alive s/p OHT	3	349054	129419	37.08	95.25	62.35	8.81	79.64	20.36	2.72	4.45	57.70	13.93	25.89	2.48	57.14	13.49	8.69	74.29	3.76	12.35	1.41	85.87	62.53	3.41	5.43	7.67	1.40	1.92	1.06
89	21	30	younger	Female	Low	3	No	Yes	BTT	HMII	alive	Alive s/p OHT	5	447309	176789	39.52	93.00	53.57	9.32	80.92	19.08	2.16	5.73	56.72	12.51	28.43	2.34	47.68	12.19	11.00	73.80	4.44	12.53	2.57	83.88	49.09	5.24	6.82	8.32	2.18	2.28	1.72
90	21	30	younger	Female	Low	3	No	Yes	BTT	HMII	alive	Alive s/p OHT	8	558290	301468	54.00	95.62	47.73	7.04	79.13	20.87	3.43	7.49	53.19	13.48	30.32	3.01	36.36	14.54	12.10	70.55	5.15	11.89	3.51	79.79	52.89	6.98	8.50	7.82	1.92	3.09	1.96
91	21	30	younger	Female	Low	3	No	Yes	BTT	HMII	alive	Alive s/p OHT	14	479402	362845	75.69	97.95	44.30	6.30	76.53	23.47	3.90	11.10	34.99	12.04	50.31	2.66	21.93	14.88	10.05	70.21	4.66	7.31	7.73	49.96	29.38	13.44	17.37	5.64	3.01	6.95	2.28
92	21	30	younger	Female	Low	3	No	Yes	BTT	HMII	alive	Alive s/p OHT	21	334942	191418	57.15	93.28	70.35	4.04	70.89	29.11	6.20	17.70	56.45	19.57	20.44	3.53	38.91	14.64	10.35	55.11	6.61	14.34	8.70	80.96	58.92	7.58	7.96	10.11	2.08	2.08	2.48
93	22	67	older	Male	High	2	Yes	NA	BTT	HMII	dead	Died	0	129980	41600	32.00	91.40	39.03	2.85	49.63	50.37	7.02	11.55	28.37	29.11	20.79	21.72	19.43	28.34	9.80	69.59	4.90	20.33	6.13	31.15	33.60	20.15	22.27	0.28	4.25	16.54	2.09
94	22	67	older	Male	High	2	Yes	NA	BTT	HMII	dead	Died	1	59478	20182	33.93	89.07	44.50	10.08	60.93	39.07	2.10	2.70	42.88	13.69	17.66	25.77	13.06	46.12	3.13	81.35	1.16	16.11	0.83	37.58	6.12	5.52	11.64	0.06	1.49	2.76	4.07
95	22	67	older	Male	High	2	Yes	NA	BTT	HMII	dead	Died	5	26646	9754	36.61	98.69	58.32	7.57	64.88	35.12	2.06	0.55	46.50	12.62	17.70	23.18	15.07	53.31	12.50	83.40	1.10	15.64	1.76	28.12	0.00	7.54	16.46	0.55	1.92	6.04	13.00
96	22	67	older	Male	High	2	Yes	NA	BTT	HMII	dead	Died	8	73641	21944	29.80	96.79	48.91	4.47	62.74	37.26	2.84	3.16	37.47	17.79	24.95	19.79	19.46	38.11	9.46	79.79	1.89	13.58	1.24	27.05	20.00	14.11	13.37	0.21	2.74	10.53	3.31
97	23	68	older	Male	High	2	No	No	BTT	HMII	alive	Alive s/p OHT	0	44004	20612	46.84	43.65	15.61	21.54	49.64	50.36	10.37	0.57	15.94	45.15	33.18	5.73	24.69	5.68	20.25	44.58	1.55	36.17	0.88	67.23	27.27	32.35	15.48	20.02	4.49	10.94	2.76
98	23	68	older	Male	High	2	No	No	BTT	HMII	alive	Alive s/p OHT	1	99731	23424	23.49	91.81	41.33	10.89	35.24	64.76	11.83	1.62	18.71	59.21	16.19	5.89	28.16	5.89	18.53	39.98	4.66	42.16	2.33	84.07	2.63	22.55	15.21	40.28	6.07	9.70	3.96
99	23	68	older	Male	High	2	No	No	BTT	HMII	alive	Alive s/p OHT	8	129637	50001	38.57	90.65	44.41	6.05	30.66	69.34	17.12	4.05	10.62	66.50	19.60	3.28	26.41	3.46	13.37	39.74	7.15	39.49	11.91	71.97	2.70	32.48	33.14	43.91	7.81	22.70	3.82
100	23	68	older	Male	High	2	No	No	BTT	HMII	alive	Alive s/p OHT	14	134368	46363	34.50	88.32	34.25	4.63	32.47	67.53	12.86	8.38	8.91	64.89	22.88	3.32	24.78	3.89	13.58	36.16	6.01	40.33	13.77	72.11	1.89	29.89	26.36	42.80	6.17	15.97	3.29
101	24	64	older	Male	High	2	No	Yes	BTT	HMII	alive	Alive s/p OHT	0	59881	8971	14.98	97.40	50.06	9.57	75.84	24.16	3.59	8.13	64.83	13.40	15.67	6.10	19.31	18.81	16.83	76.67	6.10	6.82	2.00	88.16	47.06	10.53	11.24	10.41	9.93	6.94	11.93
102	24	64	older	Male	High	2	No	Yes	BTT	HMII	alive	Alive s/p OHT	1	60193	23874	39.66	96.72	58.83	16.24	80.37	19.63	1.87	3.31	68.58	10.62	14.72	6.08	21.33	22.83	3.80	80.61	3.63	8.03	0.33	94.16	57.26	2.96	5.01	10.19	4.72	1.41	1.24
103	24	64	older	Male	High	2	No	Yes	BTT	HMII	alive	Alive s/p OHT	5	135335	27834	20.57	97.35	44.75	5.61	63.22	36.78	8.49	17.63	39.01	23.16	30.72	7.11	17.53	10.38	12.16	42.70	4.93	19.28	4.68	67.37	4.10	26.64	17.76	19.21	14.54	14.47	10.60
104	24	64	older	Male	High	2	No	Yes	BTT	HMII	alive	Alive s/p OHT	8	247113	62042	25.11	96.41	43.93	14.82	78.42	21.58	2.52	5.24	65.35	12.22	17.53	4.90	20.39	17.20	2.93	77.86	2.11	8.78	2.94	84.64	9.70	10.64	13.93	9.90	11.59	8.48	3.51
105	24	64	older	Male	High	2	No	Yes	BTT	HMII	alive	Alive s/p OHT	14	142053	56983	40.11	93.01	62.72	12.87	85.10	14.90	0.69	1.64	68.02	8.40	21.03	2.55	17.03	13.68	1.08	83.33	0.37	7.38	0.26	79.00	16.07	3.01	5.85	5.16	5.24	1.77	0.70
106	24	64	older	Male	High	2	No	Yes	BTT	HMII	alive	Alive s/p OHT	21	60384	17029	28.20	93.65	40.96	21.36	83.80	16.20	1.47	2.32	63.52	9.69	23.80	2.99	23.55	15.58	0.91	78.16	1.09	8.13	1.00	83.65	17.72	4.70	8.42	5.99	7.22	3.76	1.56
107	25	25	younger	Female	Low	3	No	Yes	BTT	HMII	alive	Alive s/p OHT	0	47645	37377	78.45	94.61	2.47	9.41	78.56	17.02	1.71	4.63	34.94	16.09	44.77	4.21	4.35	28.99	59.42	67.26	3.10	26.22	7.50	72.82	53.90	13.50	28.20	3.04	7.70	7.67	0.96
108	25	25	younger	Female	Low	3	No	Yes	BTT	HMII	alive	Alive s/p OHT	1	87452	43025	49.20	91.54	1.98	26.71	92.06	6.76	0.77	4.67	47.21	4.70	45.17	2.92	6.06	18.18	60.61	81.01	3.95	14.10	12.04	85.97	79.84	5.82	14.15	1.11	4.77	2.84	0.00
109	25	25	younger	Female	Low	3	No	Yes	BTT	HMII	alive	Alive s/p OHT	3	124056	80873	65.19	95.41	0.37	14.36	90.47	5.98	0.39	0.60	41.84	4.26	51.08	2.81	18.82	35.29	43.53	86.97	0.10	11.32	1.10	75.49	12.12	3.80	14.94	0.24	0.88	3.25	0.00
110	25	25	younger	Female	Low	3	No	Yes	BTT	HMII	alive	Alive s/p OHT	8	87840	57990	66.02	95.60	0.11	15.80	89.00	9.49	1.56	5.02	45.89	7.18	43.54	3.39	6.10	19.51	56.71	84.23	3.14	9.95	10.70	72.13	52.73	7.06	17.53	0.74	3.13	5.13	0.13
111	26	49	younger	Male	Low	3	No	NA	DT	HMII	alive	Alive	0	196315	122053	62.17	95.37	60.66	22.66	91.10	8.90	0.45	4.95	85.57	6.30	5.42	2.71	13.13	28.32	20.05	86.32	2.45	5.42	0.96	98.68	94.19	2.50	2.96	4.63	1.24	0.33	0.89
112	26	49	younger	Male	Low	3	No	NA	DT	HMII	alive	Alive	1	91909	37622	40.93	96.10	43.22	38.85	95.48	4.52	0.14	2.19	85.07	3.09	10.33	1.51	21.03	27.23	9.08	88.84	1.13	3.91	0.23	98.77	88.27	1.16	1.16	2.34	0.38	0.21	0.45
113	26	49	younger	Male	Low	3	No	NA	DT	HMII	alive	Alive	5	153762	86281	56.11	95.83	65.54	24.24	91.12	8.88	0.65	2.84	81.43	6.30	9.62	2.65	21.14	28.73	15.89	83.00	1.97	6.74	0.36	98.22	93.16	3.20	2.29	5.47	1.11	0.77	1.56
114	26	49	younger	Male	Low	3	No	NA	DT	HMII	alive	Alive	8	82406	47485	57.62	92.71	65.75	17.46	91.79	8.21	1.08	1.99	76.35	5.70	15.41	2.54	20.56	26.95	11.99	78.67	1.35	7.97	0.28	97.55	85.62	2.65	2.56	5.19	1.18	0.86	1.79
115	27	62	older	Male	High	2	No	NA	BTT	CMAG	dead	Died	0	105070	38521	36.66	98.39	47.84	12.88	60.82	39.18	11.08	2.89	33.87	29.43	26.68	10.01	19.72	21.92	2.99	59.35	14.64	20.23	3.45	81.40	46.10	24.47	24.27	24.04	11.00	17.02	1.51
116	27	62	older	Male	High	2	No	NA	BTT	CMAG	dead	Died	1	162336	73080	45.02	99.08	49.35	13.95	74.24	25.76	7.77	3.84	45.21	19.33	28.85	6.61	19.16	23.45	0.81	65.34	14.40	13.50	3.17	86.68	71.39	23.26	17.70	15.21	6.93	13.29	0.88
117	27	62	older	Male	High	2	No	NA	BTT	CMAG	dead	Died	5	111264	60487	54.36	99.15	45.94	16.13	73.16	26.84	4.64	1.82	52.04	20.07	20.94	6.95	22.93	23.47	0.50	77.11	9.30	9.58	1.20	88.92	81.82	22.20	19.01	16.98	6.89	13.57	1.36
118	27	62	older	Male	High	2	No	NA	BTT	CMAG	dead	Died	8	165870	67935	40.96	98.88	43.01	15.86	70.23	29.77	7.85	1.88	40.39	20.49	29.66	9.46	22.70	29.00	1.88	68.61	9.60	16.11	1.47	89.57	69.00	20.12	16.59	17.10	7.02	12.40	2.06
119	28	72	older	Male	Low	4	Yes	NA	BTT	HMII	dead	Died	3	424375	21971	5.18	71.78	56.98	18.53	79.84	20.16	3.08	0.51	57.91	14.07	24.06	3.97	31.74	15.95	13.65	57.94	0.65	28.51	0.17	93.57	53.33	2.87	9.69	15.61	3.87	2.16	3.99
120	28	72	older	Male	Low	4	Yes	NA	BTT	HMII	dead	Died	8	177249	103442	58.36	76.40	15.43	5.64	65.13	34.87	5.21	0.20	35.56	27.91	31.90	4.62	26.60	8.28	13.24	67.89	0.29	20.13	0.00	78.60	22.22	5.50	16.09	25.56	6.80	4.73	0.89

df.temp <- df
colnames(df.temp) <- make.names(colnames(df), unique = T)
invisible(
    suppressWarnings(
        suppressMessages(
            ggpairs(df.temp, 
                    mapping = aes(color = Survival), 
                    columns = colnames(df.temp)[c(2,4,6,7,8,9,10,11)]) 
        )))

Correlation and Clusters

We computed the cross correlation matrix, and clustered the B-cells. We found one large block, and one smaller block of correlated B-cells; the remaining B-cells were largely uncorrelated.

require(pheatmap, quietly = T)
require(RColorBrewer, quietly = T)
r <- cor(df[,c(2,14:42)], use = "p")
pheatmap(r, 
         color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(100),
         breaks = seq(-1, 1, length.out = 100),
         fontsize = 7,
         cutree_rows = 5,
         cutree_cols = 5
)

Principal Component Analysis

We found large variability between individual patients, as well as device types, using PCA.

suppressMessages(require(ggbiplot, quietly = T))
require(ggsci, quietly = T)

# Efron's double standardization
double_standardize <- function(x, niter = 100) {
    for(i in 1:niter) x <- t(scale(t(scale(x))))
    return(as.data.frame(x))
}

isna <- unique(unlist(apply(df[,14:42], 2, function(x) which(is.na(x)))))
pca <- prcomp(double_standardize(df[-isna, 14:42]), center = TRUE, scale. = TRUE)

colorfun <- function(grouping, ...){
    if(nlevels(factor(grouping)) > 10) scale_color_discrete(...)
    if(nlevels(factor(grouping)) <=10) scale_color_d3(...)
} 

plots.pca <- mclapply(names(df)[1:17], function(this_var){
    this_groups <- df[-isna, this_var]
    if(is.numeric(this_groups)) this_groups <- cut(this_groups, 4)
    
    ggbiplot(pca,
             groups = this_groups,
             ellipse = TRUE,
             alpha = 0.3,
             varname.size = 1.2) + 
        colorfun(this_groups, name = this_var) +
        ggtitle(paste0(this_var, " variability")) +
        theme_classic() 
}, mc.cores = detectCores()-1)

names(plots.pca) <- names(df)[1:17]
#plots.pca$PatientID
#plots.pca$`Device Type`

for(ii in 1:length(plots.pca)){
     cat("  \n##", names(plots.pca)[ii], "\n")
     suppressWarnings(print(plots.pca[[ii]]))
     cat("  \n")
}

PatientID

Age

AgeGreater60

Sex

LowIntermacs

InterMACS

RVAD

Sensitized

VAD Indication

Device Type

Survival

Outcome

Time

num Total PBMC

num lymph

lymph

live lymph

Device Types

We analyzed the devices for correlations with the other variables.

Fisher’s exact test

We analyzed the dependency of device types on the other discrete variables using Fisher’s exact test. We found several dependencies (Benjamini-Hochberg \(q<0.05\)), shown below.

isfactor <- which(sapply(df, is.factor))[-1]
contingency <- lapply(isfactor[names(isfactor) != "Device Type"], 
                      function(this_factor){
                          table(df[,this_factor], df$`Device Type`)
                      })

fisher.p <- sapply(contingency, function(this_table){
    fisher.test(this_table, simulate.p.value = T, B = 10000)$p
})
fisher.q <- p.adjust(fisher.p, method = "BH")

signif.ix <- which(fisher.q < 0.05)
signif.order <- sort(fisher.q[signif.ix], index.return = T)$ix
for(this_ix in signif.ix[signif.order]){
    cat("  \n###", names(contingency)[this_ix], "\n")
    cat(paste0("Benjamini-Hochberg qvalue = ", signif(fisher.q[this_ix], 2)),
        ". \n")
    print(kable(contingency[this_ix][[1]],  row.names = T) %>%
        kable_styling(bootstrap_options = c("striped", 
                                        "hover", 
                                        "condensed",
                                        "responsive"),
                     font_size = 12)
    )
    cat("  \n")
}

AgeGreater60

Benjamini-Hochberg qvalue = 0.00011 .

	CMAG	HMII	HVAD	PVAD	TAH
younger	0	56	7	21	7
older	7	84	7	0	7

Sex

Benjamini-Hochberg qvalue = 0.00011 .

	CMAG	HMII	HVAD	PVAD	TAH
Female	0	35	0	14	0
Male	7	105	14	7	14

LowIntermacs

Benjamini-Hochberg qvalue = 0.00011 .

	CMAG	HMII	HVAD	PVAD	TAH
Low	0	63	14	0	0
High	7	77	0	21	14

InterMACS

Benjamini-Hochberg qvalue = 0.00011 .

	CMAG	HMII	HVAD	PVAD	TAH
1	0	7	0	0	7
2	7	70	0	21	7
3	0	56	14	0	0
4	0	7	0	0	0

RVAD

Benjamini-Hochberg qvalue = 0.00011 .

	CMAG	HMII	HVAD	PVAD	TAH
No	7	105	14	0	0
Yes	0	35	0	21	14

VAD Indication

Benjamini-Hochberg qvalue = 0.00011 .

	CMAG	HMII	HVAD	PVAD	TAH
BTT	7	98	14	21	14
DT	0	42	0	0	0

Survival

Benjamini-Hochberg qvalue = 0.00011 .

	CMAG	HMII	HVAD	PVAD	TAH
alive	0	98	14	7	7
dead	7	42	0	14	7

Outcome

Benjamini-Hochberg qvalue = 0.00011 .

	CMAG	HMII	HVAD	PVAD	TAH
Alive	0	28	0	0	0
Alive s/p OHT	0	70	14	7	7
Died	7	35	0	7	0
Died post OHT	0	7	0	0	0
Died s/p OHT	0	0	0	7	7

Sensitized

Benjamini-Hochberg qvalue = 0.017 .

	CMAG	HMII	HVAD	PVAD	TAH
No	0	42	7	0	7
Yes	0	28	7	7	7

Mixed Effect Model

We attempted to analyze the differences in B-cell levels across device types using a mixed effect model (a.k.a. two-way repeated measures ANOVA). Here we report variables that had a statistically significant variance (Benjamini-Hochberg \(q<0.05\)) across devices, times, or their interaction. As there were 5 different device types, this model is quite underpowered, and the results are probably spurious. However, we reported them for the sake of completeness.

suppressMessages(require(lmerTest, quietly = T))
suppressMessages(require(car, quietly = TRUE))

df.device <- df
colnames(df.device) <- make.names(colnames(df), unique = T)

varnames <- colnames(df.device)[14:42]
models.device <- mclapply(varnames, function(this_var){
    this_formula <- as.formula(paste0(this_var, " ~ Device.Type * factor(Time) + (1|PatientID)"))
    invisible(suppressMessages(this_model <- lmer(this_formula, data = df.device)))
    this_anova <- Anova(this_model, type = 2)
    pvals <- this_anova$`Pr(>Chisq)`[c(1,2,3)]
    return(list(model = this_model,
                pvals = pvals))
}, mc.cores = detectCores()-1)
names(models.device) <- colnames(df)[14:42]

pvals <- do.call(rbind, lapply(models.device, function(x) x$pvals))
rownames(pvals) <- varnames
colnames(pvals) <- c("Device", "factor(Time)", "Device:factor(Time)")

qBH <- matrix(p.adjust(pvals, method = "BH"), nrow = nrow(pvals))
rownames(qBH) <- colnames(df)[14:42]
colnames(qBH) <- colnames(pvals)

sigvars <- apply(apply(qBH, 2, function(x) x<=0.05), 1, function(x) sum(x) > 0)
sig.qtable <- qBH[sigvars,][order(apply(qBH[sigvars,], 1, min)),]

qtable <- as.data.frame(signif(sig.qtable, 2))
qtable %>%
    mutate(
        `B-cell` = row.names(.),
        Device = cell_spec(Device, color = ifelse(qtable$Device > 0.05, "grey", "red")),
        `Device:factor(Time)` = cell_spec(`Device:factor(Time)`, color = ifelse(qtable$`Device:factor(Time)` > 0.05, "grey", "red")),
        `factor(Time)` = cell_spec(`factor(Time)`, color = ifelse(qtable$`factor(Time)` > 0.05, "grey", "red"))
    ) %>%
    kable( escape = F,
           digits = 20,
           row.names = T,
           caption = "Significant device-type q-values") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                  font_size = 12) %>%
    scroll_box(width = "100%", height = "300px")

Significant device-type q-values

	Device	factor(Time)	Device:factor(Time)	B-cell
1	0.16	0.071	3e-10	CD19+CD5+
2	0.33	0.041	8.7e-06	CD27+IgD- switched memory
3	0.54	0.071	7.8e-05	CD19+CD27+
4	0.54	0.31	7.8e-05	CD27-IgD- switched memory
5	0.59	4e-04	0.0017	CD19+CD11b+
6	0.071	0.16	0.0034	CD27-IgD+ mature naive
7	0.93	0.0058	0.98	CD19 of live lymph
8	0.93	0.94	0.012	CD19CD24hiCD38-memory
9	0.27	0.041	1	CD27+IgD-IgM+ switched memory

Figures

We plotted the average across time for each of the B-cells that showed a statistically significant effect across devices in the above mixed effect models. We draw attention to the features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the multivariate fit.

require(reshape2, quietly = T)
df.long <- melt(df, id.vars = colnames(df)[1:13])
groups <- make.names(c("Device Type"))
names(df.long) <- make.names(names(df.long))

invisible(suppressMessages(require(Hmisc, quietly = T)))
stat_sum_df <- function(fun, geom="errorbar", ...) {
    stat_summary(fun.data = fun, geom = geom, width = 1, ...)
}

plots.ts <- mclapply(rownames(qtable), function(this_var){
    lapply(groups, function(this_groups){
        ggplot(subset(df.long, df.long$variable == this_var)) +
            aes(x = Time, y = value, group = PatientID) +
            aes_string(color = this_groups, fill = this_groups) +
            geom_line(alpha = 0) + 
            geom_point(alpha = 0) + 
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("point"), position = position_dodge(.5)) + 
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("line"), position = position_dodge(.5)) + 
            stat_sum_df(function(x) mean_cl_boot(x, conf.int = 0.68), mapping = aes_string(group = this_groups), position = position_dodge(.5)) + 
            #stat_smooth(aes_string(group = this_groups), method = "loess", span = 1) +
            scale_color_d3() + scale_fill_d3() +
            xlab("Time (days after surgery)") +
            ylab(this_var) + 
            ggtitle(paste(this_var)) +
            theme_classic()
    })
}, mc.cores = detectCores()-1)
names(plots.ts) <- rownames(qtable)

for(ii in c(1:length(plots.ts))){
    for(jj in 1:length(plots.ts[[ii]])){
        sumtable <- suppressMessages(summary(models.device[[rownames(qtable)[ii]]]$model))
        sumtable <- sumtable$coefficients[-1, c(4:5)] # drop intercept
        sigsum <- sumtable[sumtable[,2] <= 0.05, , drop = F]
        cat("  \n###", rownames(qtable)[ii], "\n")
        print(kable(sigsum, row.names = T) %>%
            kable_styling(bootstrap_options = c("striped", 
                                                "hover", 
                                                "condensed",
                                                "responsive"),
                          font_size = 12)
        )
        cat("  \n")
        suppressWarnings(print(plots.ts[[ii]][[jj]]))
        cat("  \n")
    }
}

CD19+CD5+

	t value	Pr(>|t|)
Device.TypePVAD:factor(Time)3	-3.212790	0.0019851
Device.TypeTAH:factor(Time)5	5.597892	0.0000004

CD27+IgD- switched memory

	t value	Pr(>|t|)
Device.TypeHVAD:factor(Time)1	2.193867	0.0316454
Device.TypePVAD:factor(Time)3	-4.506401	0.0000260

CD19+CD27+

	t value	Pr(>|t|)
Device.TypeHVAD:factor(Time)1	2.288402	0.0252039
Device.TypePVAD:factor(Time)3	-4.140293	0.0000955

CD27-IgD- switched memory

	t value	Pr(>|t|)
factor(Time)14	2.658002	0.0095496
Device.TypeTAH:factor(Time)5	4.417748	0.0000377
Device.TypeHMII:factor(Time)14	-2.262837	0.0264767
Device.TypeHVAD:factor(Time)14	-2.248730	0.0274789
Device.TypePVAD:factor(Time)14	-2.015937	0.0472975

CD19+CD11b+

	t value	Pr(>|t|)
factor(Time)14	5.663951	0.0000003
factor(Time)21	2.286686	0.0252983
Device.TypeHMII:factor(Time)3	-2.083219	0.0408780
Device.TypePVAD:factor(Time)3	-2.149256	0.0350169
Device.TypeHMII:factor(Time)14	-5.250946	0.0000015
Device.TypeHVAD:factor(Time)14	-4.341077	0.0000459
Device.TypePVAD:factor(Time)14	-2.999221	0.0037102

CD27-IgD+ mature naive

	t value	Pr(>|t|)
factor(Time)14	-3.556174	0.0006729
Device.TypeTAH:factor(Time)1	-2.304827	0.0241998
Device.TypeTAH:factor(Time)5	-4.252348	0.0000659
Device.TypeTAH:factor(Time)8	-2.129620	0.0367838
Device.TypeHMII:factor(Time)14	2.953963	0.0042493
Device.TypeHVAD:factor(Time)14	2.846370	0.0057844
Device.TypePVAD:factor(Time)14	2.624952	0.0105914

CD19 of live lymph

	t value	Pr(>|t|)

CD19CD24hiCD38-memory

	t value	Pr(>|t|)
Device.TypeHVAD:factor(Time)1	2.07174	0.042064

CD27+IgD-IgM+ switched memory

	t value	Pr(>|t|)

HeartMate-II

The HeartMate-II (HMII) recipients were the largest group, and we analyzed them by themselves due to the previously observed variability across devices.

require(reshape2, quietly = T)
df.HMII <- subset(df, df$`Device Type`=="HMII")
df.long <- melt(df.HMII, id.vars = colnames(df)[1:13])

groups <- make.names(c("AgeGreater60", 
                       "Sex",
                       "LowIntermacs",
                       "RVAD", 
                       "Sensitized",
                       "VAD Indication", 
                       #"Device Type", 
                       "Survival",
                       "Outcome"))

names(df.long) <- make.names(names(df.long))

plots.ts <- mclapply(unique(df.long$variable), function(this_var){
    lapply(groups, function(this_groups){
        ggplot(subset(df.long, df.long$variable == this_var)) +
            aes(x = Time, y = value, group = PatientID) +
            aes_string(color = this_groups, fill = this_groups) +
            geom_line(alpha = 0) + 
            geom_point(alpha = 0) + 
            #stat_smooth(aes_string(group = this_groups), method = "loess", span = 1) +
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("point"), position = position_dodge(.5)) + 
            stat_summary(fun.y = mean, aes_string(group = this_groups), geom=c("line"), position = position_dodge(.5)) + 
            stat_sum_df(function(x) mean_cl_boot(x, conf.int = 0.68), mapping = aes_string(group = this_groups), position = position_dodge(.5)) + 
            scale_color_aaas() + scale_fill_aaas() +
            xlab("Time (days after surgery)") +
            ylab(this_var) + 
            ggtitle(paste(this_var)) +
            theme_classic()
    })
}, mc.cores = detectCores()-1)
names(plots.ts) <- unique(df.long$variable)

# for(ii in c(1:length(plots.ts))){
#     for(jj in 1:length(plots.ts[[ii]])){
#         suppressWarnings(print(plots.ts[[ii]][[jj]]))
#     }
# } 

Principal Component Analysis

We again found large variability between individual patients using PCA.

suppressMessages(require(ggbiplot, quietly = T))
require(ggsci, quietly = T)

# Efron's double standardization
double_standardize <- function(x, niter = 100) {
    for(i in 1:niter) x <- t(scale(t(scale(x))))
    return(as.data.frame(x))
}

isna <- unique(unlist(apply(df.HMII[,14:42], 2, function(x) which(is.na(x)))))
pca <- prcomp(double_standardize(df.HMII[-isna, 14:42]), center = TRUE, scale. = TRUE)

colorfun <- function(grouping, ...){
    if(nlevels(factor(grouping)) > 10) scale_color_discrete(...)
    if(nlevels(factor(grouping)) <=10) scale_color_d3(...)
} 

plots.pca <- mclapply(names(df.HMII)[1:17], function(this_var){
    this_groups <- df.HMII[-isna, this_var]
    if(is.numeric(this_groups)) this_groups <- cut(this_groups, 4)
    
    ggbiplot(pca,
             groups = this_groups,
             ellipse = TRUE,
             alpha = 0.3,
             varname.size = 1.2) + 
        colorfun(this_groups, name = this_var) +
        ggtitle(paste0(this_var, " variability")) +
        theme_classic() 
}, mc.cores = detectCores()-1)

names(plots.pca) <- names(df.HMII)[1:17]
#plots.pca$PatientID
#plots.pca$`Device Type`

for(ii in 1:length(plots.pca)){
     cat("  \n###", names(plots.pca)[ii], "\n")
     suppressWarnings(print(plots.pca[[ii]]))
     cat("  \n")
}

PatientID

Age

AgeGreater60

Sex

LowIntermacs

InterMACS

RVAD

Sensitized

VAD Indication

Device Type

Survival

Outcome

Time

num Total PBMC

num lymph

lymph

live lymph

Mixed Effect Model

We attempted to analyze the differences in B-cell levels for various features using a mixed effect model (a.k.a. two-way repeated measures ANOVA). Here we report variables that had a statistically significant variance (Benjamini-Hochberg \(q<0.05\)) across devices, times, or their interaction.

suppressMessages(require(lmerTest, quietly = TRUE))
suppressMessages(require(car, quietly = TRUE))
require(reshape2, quietly = TRUE)

df.lmer <- df.HMII
names(df.lmer) <- make.names(names(df.lmer), unique = TRUE)

groupvars.ix <- c(3,4,5,7,8,9,11)
groupvars <- names(df.lmer)[groupvars.ix]

bcells.ix <- c(14:42)
bcells <- names(df.lmer)[bcells.ix]

pvals <- mclapply(groupvars, function(this_groupvar){
    these_pvalues <- lapply(bcells, function(this_bcell){
        this_formula <- as.formula(paste0(this_bcell, " ~ ", this_groupvar, 
                                          " * factor(Time) + (1|PatientID)"))
        suppressMessages(suppressWarnings(this_model <- lmer(this_formula, data = droplevels(df.lmer))))
        this_anova <- Anova(this_model, type = 2)
        this_pvalues <- this_anova$`Pr(>Chisq)`
        names(this_pvalues) <- rownames(this_anova)
        return(this_pvalues)
    })
    pvalues <- do.call(rbind, these_pvalues)
    rownames(pvalues) <- bcells
    return(pvalues)
}, mc.cores = detectCores()-1)
names(pvals) <- groupvars
pvals.matrix <- do.call(cbind, lapply(pvals, function(this_pval) this_pval[,c(1,3)]))

# Benjamini Hochberg
# qBH <- matrix(p.adjust(as.numeric(pvals.matrix), 
#                        method = "BH"), 
#               nrow = nrow(pvals.matrix), 
#               ncol = ncol(pvals.matrix), 
#               byrow = F) 
# rownames(qBH) <- rownames(pvals.matrix)
# colnames(qBH) <- colnames(pvals.matrix)
# rownames(qBH) <- names(df)[bcells.ix]
# qvalsBH.df <- melt(qBH)
# colnames(qvalsBH.df) <- c("B-cell", "parameter", "qvalue")
# qvalsBH.df.ranked <- qvalsBH.df[order(qvalsBH.df$qvalue, decreasing = F),]
# qvalsBH.df.ranked[qvalsBH.df.ranked$qvalue <= 0.3,]

# Local FDR
require(fdrtool, quietly = T)
invisible(suppressMessages(fdrobj <- fdrtool(as.numeric(pvals.matrix), statistic = "pvalue", plot = F, verbose = F)))
qvals.matrix <- matrix(fdrobj$q, nrow = nrow(pvals.matrix), ncol = ncol(pvals.matrix), byrow = F)
rownames(qvals.matrix) <- rownames(pvals.matrix)
colnames(qvals.matrix) <- colnames(pvals.matrix)
rownames(qvals.matrix) <- names(df)[bcells.ix]
qvals.df <- melt(qvals.matrix)
colnames(qvals.df) <- c("B-cell", "parameter", "qvalue")
qvals.df.ranked <- qvals.df[order(qvals.df$qvalue, decreasing = F),]
shortlist <- qvals.df.ranked[qvals.df.ranked$qvalue <= 0.3,]


kable(shortlist, 
      digits = 3,
      row.names = T,
      caption = "Significant results") %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
                  font_size = 10) %>%
    scroll_box(width = "100%", height = "300px")

Significant results

	B-cell	parameter	qvalue
2	num lymph	AgeGreater60	0.040
145	CD19+27+IgD-38++IgG ASC	LowIntermacs	0.040
52	CD268 of +27-38++transitional	AgeGreater60:factor(Time)	0.073
276	CD27+IgD-IgM+ switched memory	Sensitized:factor(Time)	0.081
61	lymph	Sex	0.200
348	CD19+27+IgD-38++IgG ASC	VAD.Indication:factor(Time)	0.213
232	CD19+27+IgD-38++IgG ASC	RVAD:factor(Time)	0.230
155	CD27-38++ transitional	LowIntermacs:factor(Time)	0.254
3	lymph	AgeGreater60	0.257

Figures

We plotted the average across time for each of the B-cells that showed a statistically significant effect across various factors in the above mixed effect models. We draw attention to the features that induced the positive test result, by listing the model parameters with \(p<0.05\) in the multivariate fit.

require(stringr, quietly = T)
siggroups <- sapply(str_split(shortlist$parameter, ":"), function(x) x[1])
for(ii in 1:nrow(shortlist)){
    this_group <-siggroups[ii]
    cat("  \n###", as.character(shortlist$`B-cell`[ii]), "\n")
    suppressWarnings(print(plots.ts[[shortlist$`B-cell`[ii]]][[which(groups == this_group)]]))
    cat("  \n")
}

num lymph

CD19+27+IgD-38++IgG ASC

CD268 of +27-38++transitional

CD27+IgD-IgM+ switched memory

lymph

CD19+27+IgD-38++IgG ASC

CD19+27+IgD-38++IgG ASC

CD27-38++ transitional

lymph